Category Archives : Artificial Intelligence

09

Aug

What is Artificial Intelligence?
What is Artificial Intelligence?

It has been said that Artificial Intelligence will define the next generation of software solutions. If you are even remotely involved with technology, you will almost certainly have heard the term with increasing regularity over the last few years. It is likely that you will also have heard different definitions for Artificial Intelligence offered, such as:

“The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” – Encyclopedia Britannica

“Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.” – Wikipedia

How useful are these definitions? What exactly are “tasks commonly associated with intelligent beings”? For many people, such definitions can seem too broad or nebulous. After all, there are many tasks that we can associate with human beings! What exactly do we mean by “intelligence” in the context of machines, and how is this different from the tasks that many traditional computer systems are able to perform, some of which may already seem to have some level of intelligence in their sophistication? What exactly makes the Artificial Intelligence systems of today different from sophisticated software systems of the past?

It could be argued that any attempt to try

06

Aug

Accelerate healthcare initiatives with Azure UK NHS blueprints

Today, the healthcare industry is confronting many complex and daunting challenges that include demands to:

Increase patient engagement. Take advantage of big data, analytics, artificial Intelligence (AI), and machine learning (ML). Integrate consumer health apps, wearables, and the Internet of Medical Things (IoMT). Combat cybersecurity threats, breaches, and ransomware.

In the midst of this, however, healthcare organizations must continue to:

Deliver the best patient care. Improve patient outcomes. Reduce healthcare costs (now 7 percent of GDP in the UK and almost 18 percent of GDP in the United States). Enhance patient and clinician experiences.

And all with limited budget and resources!

Cloud computing can help healthcare organizations focus on patient care and reducing costs, and it enables IT to be more flexible, agile, scalable, and secure as the healthcare industry changes and grows.

A key challenge to adopting cloud computing is that healthcare needs solutions, not IT projects. Healthcare organizations of every size often have limited IT and cybersecurity resources burdened with maintaining existing IT infrastructure.

So how can they create new solutions?

Rx: Blueprints

To rapidly acquire new capabilities and implement new solutions, healthcare IT and developers can now take advantage of industry-specific Azure Blueprints. These are packages that

06

Aug

Accelerate healthcare initiatives with Azure UK NHS blueprints

Today, the healthcare industry is confronting many complex and daunting challenges that include demands to:

Increase patient engagement. Take advantage of big data, analytics, artificial Intelligence (AI), and machine learning (ML). Integrate consumer health apps, wearables, and the Internet of Medical Things (IoMT). Combat cybersecurity threats, breaches, and ransomware.

In the midst of this, however, healthcare organizations must continue to:

Deliver the best patient care. Improve patient outcomes. Reduce healthcare costs (now 7 percent of GDP in the UK and almost 18 percent of GDP in the United States). Enhance patient and clinician experiences.

And all with limited budget and resources!

Cloud computing can help healthcare organizations focus on patient care and reducing costs, and it enables IT to be more flexible, agile, scalable, and secure as the healthcare industry changes and grows.

A key challenge to adopting cloud computing is that healthcare needs solutions, not IT projects. Healthcare organizations of every size often have limited IT and cybersecurity resources burdened with maintaining existing IT infrastructure.

So how can they create new solutions?

Rx: Blueprints

To rapidly acquire new capabilities and implement new solutions, healthcare IT and developers can now take advantage of industry-specific Azure Blueprints. These are packages that

02

Aug

Real example: improve accuracy, reduce training times for existing R codebase

When you buy an item on a favored website, does the site show you pictures of what others have bought? That’s the result of a recommendation system. Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it’s time to consider improving performance and scalability by moving these systems to the cloud —the Azure cloud.

Problem: to re-host and optimize an existing R model in Azure

Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished. We worked together to find a way to significantly improve the model training time using parallel R algorithms. Then we worked to streamline how they operationalized their R model. All the work was done using libraries available with Microsoft Machine Learning Server (R Server). 

The architecture: Azure SQL + Machine Learning Server

There are several components and steps to the solution. We needed a database

02

Aug

Real example: improve accuracy, reduce training times for existing R codebase

When you buy an item on a favored website, does the site show you pictures of what others have bought? That’s the result of a recommendation system. Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it’s time to consider improving performance and scalability by moving these systems to the cloud —the Azure cloud.

Problem: to re-host and optimize an existing R model in Azure

Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished. We worked together to find a way to significantly improve the model training time using parallel R algorithms. Then we worked to streamline how they operationalized their R model. All the work was done using libraries available with Microsoft Machine Learning Server (R Server). 

The architecture: Azure SQL + Machine Learning Server

There are several components and steps to the solution. We needed a database

26

Jul

Current use cases for machine learning in healthcare

Machine learning (ML) is causing quite the buzz at the moment, and it’s having a huge impact on healthcare. Payers, providers, and pharmaceutical companies are all seeing applicability in their spaces and are taking advantage of ML today. This is a quick overview of key topics in ML, and how it is being used in healthcare.

A machine learning model is created by feeding data into a learning algorithm. The algorithm is where the magic happens. There are algorithms to detect a patient’s length of stay based on diagnosis, for example. Someone had to write that algorithm and then train it with true and reliable data. Over time, the model can be re-trained with newer data, increasing the model’s effectiveness.

Machine learning on Azure

Machine learning is a subset of Artificial Intelligence (AI). AI can be thought of as a using a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. I won’t go into more detail on the distinction, but here are some resources to help you get started.

Azure Machine Learning services enable building, deploying, and managing machine learning and AI models using any Python tools

25

Jul

Security Center’s adaptive application controls are generally available

Azure Security Center provides several threat prevention mechanisms to help you reduce surface areas susceptible to attack. One of those mechanisms is adaptive application controls. Today we are excited to announce the general availability of this capability, which helps you audit and block unwanted applications.

Adaptive application controls help you define the set of applications that are allowed to run on configured groups of virtual machines (VM). Enabling adaptive application controls for your VMs. Azure Security Center will allow you to do a few things. First, it recommends applications (EXEs, MSIs, and Scripts) for whitelisting, automatically clustering similar VMs to ease manageability and reduce exposure to unnecessary applications. It also applies the appropriate rules in an automated fashion, monitors any violations of those rules, and enables you to manage and edit previously applied application whitelisting policies.

By default, Security Center enables application control in Audit mode. After validating that the whitelist has not had any adverse effects on your workload, you can change the protection mode to Enforce mode through the Security Center management UI.

You can also change the application control policy for each configured group of VMs through the same Security Center management UI, edit and

25

Jul

Security Center’s adaptive application controls are generally available

Azure Security Center provides several threat prevention mechanisms to help you reduce surface areas susceptible to attack. One of those mechanisms is adaptive application controls. Today we are excited to announce the general availability of this capability, which helps you audit and block unwanted applications.

Adaptive application controls help you define the set of applications that are allowed to run on configured groups of virtual machines (VM). Enabling adaptive application controls for your VMs. Azure Security Center will allow you to do a few things. First, it recommends applications (EXEs, MSIs, and Scripts) for whitelisting, automatically clustering similar VMs to ease manageability and reduce exposure to unnecessary applications. It also applies the appropriate rules in an automated fashion, monitors any violations of those rules, and enables you to manage and edit previously applied application whitelisting policies.

By default, Security Center enables application control in Audit mode. After validating that the whitelist has not had any adverse effects on your workload, you can change the protection mode to Enforce mode through the Security Center management UI.

You can also change the application control policy for each configured group of VMs through the same Security Center management UI, edit and

23

Jul

IoT: the catalyst for better risk management in insurance

Thought leader Matteo Carbone has titled his book All the Insurance Players Will Be Insurtech. He means that insurance companies that embrace digital transformation and technologies will lead the industry. Those technologies include the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data. Carbone believes that the use of new technologies gives insurers “superpowers” to assess risk more accurately, manage risk continually, and mitigate risk in real-time.

The process of getting superpowers is the process of converting IoT data into actionable insights, and using those insights to reduce risk through prevention and mitigation of claim events. As the powers grow, so do the benefits to insurance customers and providers. Insurers can also increase the pace of  customer interaction. It is the growth in the number of interactions produces more data points, and the same data is used to prevent or mitigate risk, while driving the sale of additional services outside the traditional insurance value chain. Remote monitoring and emergency alert services also provide peace-of-mind to the customer. These are only the start of additional services. Insurance companies are now selling a matrix of other services layered on top of the base policy. The income of

23

Jul

IoT: the catalyst for better risk management in insurance

Thought leader Matteo Carbone has titled his book All the Insurance Players Will Be Insurtech. He means that insurance companies that embrace digital transformation and technologies will lead the industry. Those technologies include the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data. Carbone believes that the use of new technologies gives insurers “superpowers” to assess risk more accurately, manage risk continually, and mitigate risk in real-time.

The process of getting superpowers is the process of converting IoT data into actionable insights, and using those insights to reduce risk through prevention and mitigation of claim events. As the powers grow, so do the benefits to insurance customers and providers. Insurers can also increase the pace of  customer interaction. It is the growth in the number of interactions produces more data points, and the same data is used to prevent or mitigate risk, while driving the sale of additional services outside the traditional insurance value chain. Remote monitoring and emergency alert services also provide peace-of-mind to the customer. These are only the start of additional services. Insurance companies are now selling a matrix of other services layered on top of the base policy. The income of